Last year I sat on a couple of panels organised by I’m a Scientist’s Shane McCracken at various science communication conferences. A couple of days ago, I noticed Shane had popped up a post asking Who are you Twitter?, a quick review of a social media mapping exercise carried out on the followers of the @imascientist Twitter account.

Thinking back to the context of evaluating the impact of events that include social media as part of the overall campaign, it struck me that whilst running a particular event may not lead to a huge surge in follower numbers on the day of the event or in the immediate aftermath, the followers who do sign up over that period might have signed up as a result of the event. And now we have the first inklings of a post hoc analysis tool that lets us try to identify these people, and perhaps look to see if their profiles are different to profiles of followers who signed up at different times (maybe reflecting the audience interest profile of folk who attended a particular event, or reflecting sign ups from a particular geographical area?)

In other words, through generating the follower acquisition curve, can we use it to filter down to folk who started following around a particular time in order to then see whether there is a possibility that they started following as a result of a particular event, and if so can count as some sort of “conversion”? (I appreciate that there are a lot of caveats in there!;-)

A similar approach may also be relevant in the context of analysing link building around historical online community events, such as MOOCs… If we know somebody took a particular MOOC at a particular time, might we be able to construct their follower acquisition curve and then analyse it around the time of the MOOC, looking to see if the connections built over that period are different to the users other followers, and as such may represent links developed as a result of taking the MOOC? Analysing the timelines of the respective parties may further reveal conversational dynamics between those parties, and as such allow is to see whether a fruitful social learning relationship developed out of contact made in the MOOC?

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Thank you for posting this. It’s sparking some other interesting questions for me.

Our events are March and June and anecdotally we do see a noticeable uplift in the rate of follows because we are active and our followers RT a lot.

The question of measuring impact is still pertinent to me. Again anecdotally we get the impression that a significant proportion of participants in our events start using twitter (or start using it more) as a result of taking part and that they carry on networking afterwards.

Does the chart above capture how early on a follower’s use of twitter they started following us and whether they gone on to use it more?

Shane – There are certain methodological issues associated with profiling accounts that have only small numbers of followers. I’ll do a follow on a post reviewing some of them, hopefully for tomorrow… (no time right now…)

Just thinking about the MOOC side of things. I know my own followers have increased through participation on a number of MOOCs, but the numbers are so small that they probably wouldn’t be viable for this type of analysis. But thinking about where the big numbers might be and where “fruitful social learning relationships” might develop where would the big numbers be? Probably with course announcement type tweets, not quite a bot but more an admin type account for a course, which might not actually provide any significant learning relationships but might be very valuable in terms of course admin and keeping people aware of developments. . The @hybridped account was pretty active during #moocmooc tho.